{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel SHAP explanation for multinomial logistic regression models "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Note\n",
    "    \n",
    "To enable SHAP support, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[shap]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shap.summary_plot currently doesn't work with matplotlib>=3.6.0,\n",
    "# see bug report: https://github.com/slundberg/shap/issues/2687\n",
    "!pip install matplotlib==3.5.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a previous [example](kernel_shap_wine_intro.ipynb), we showed how the KernelSHAP algorithm can be aplied to explain the output of an arbitrary classification model so long the model outputs probabilities or operates in margin space. We also showcased the powerful visualisations in the `shap` library that can be used for model investigation. In this example we focus on understanding, in a simple setting, how conclusions drawn from the analysis of the KernelShap output relate to conclusions drawn from interpreting the model directly. To make this possible, we fit a logistic regression model on the Wine dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from alibi.explainers import KernelShap\n",
    "from scipy.special import logit\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation: load and split Wine dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = wine.data\n",
    "target = wine.target\n",
    "target_names = wine.target_names\n",
    "feature_names  = wine.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data into testing and training sets and normalize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training records: 142\n",
      "Testing records: 36\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, \n",
    "                                                    target, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=0,\n",
    "                                                   )\n",
    "print(\"Training records: {}\".format(X_train.shape[0]))\n",
    "print(\"Testing records: {}\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_norm = scaler.transform(X_train)\n",
    "X_test_norm = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a multinomial logistic regression classifier to the Wine dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(multi_class=&#x27;multinomial&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(multi_class=&#x27;multinomial&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(multi_class='multinomial', random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(multi_class='multinomial',\n",
    "                                random_state=0,\n",
    "                                )\n",
    "classifier.fit(X_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Confusion matrix for the logistic regression classifier'\n",
    "disp = ConfusionMatrixDisplay.from_estimator(classifier, \n",
    "                                             X_test_norm, \n",
    "                                             y_test,\n",
    "                                             display_labels=target_names,\n",
    "                                             cmap=plt.cm.Blues,\n",
    "                                             normalize=None,\n",
    "                                            )\n",
    "disp.ax_.set_title(title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the logistic regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='src_2'></a>\n",
    "\n",
    "One way to arrive at the multinomial logistic regression model is to consider modelling a categorical response \n",
    "variable $y \\sim \\text{Cat} (y| \\beta x)$ where $\\beta$ is $K \\times D$ matrix of distribution parameters with $K$ being the number of classes and $D$ the feature dimensionality. Because the probability of outcome $k$ being observed given $x$, $p_{k} = p(y=k|x, \\mathbf{\\beta})$, is bounded by $[0, 1]$, the logistic regression assumes that a linear relationship exists between the *logit* transformation of the output and the input. This can be formalised as follows:\n",
    "\n",
    "$$\n",
    "\\log \\left( \\frac{p_{k}}{1 - p_{k}} \\right) = \\beta_{0,k} + \\beta_{1,k} x_{1}  + \\beta_{2,k} x_{2} + \\cdots + \\beta_{D,k} x_{D} = \\mathbf{\\beta}_k \\cdot x\n",
    "$$\n",
    "\n",
    "\n",
    "The RHS is a function of the expected value of the categorical distribution (sometimes referred to as a _link function_ in the literature). The coefficients $\\beta$ of the linear relations used to fit the logit transformation are estimated jointly given a set of training examples $\\mathcal{D}=  \\{(x_i, y_i)\\}_{i=1}^N$. \n",
    "\n",
    "For each class, the vector of coefficients $\\mathbb{\\beta}_k$ can be used to interpret the model *globally*; in the absence of interaction terms, the coefficient of a predictor (i.e.,  independent variable) represents the *change in log odds* when the predictor changes by one unit while all other variables are kept at fixed values. Equivalently, the exponentiated coefficient is equivalent to a change in odds. Since the transformation from odds to outcome probabilities is monotonic, a change in odds also implies a change in the outcome probability in the same direction. Thus, the magnitudes of the feature coefficients measure the effect of a predictor on the output and thus one can globally interpret the logistic regression model.\n",
    "\n",
    "However, the log odds ratios and odds ratios are known to be sensitive to *unobserved heterogenity*, that is, omission of a variable with good explanatory power from a logistic regression model *assumed true*. While we will not be concerned directly with this issue and refer the interested reader to [[2]](#References), we will be using the *estimated percentage unit effect* (or the *marginal effect*)\n",
    "\n",
    "$$\n",
    "\\beta_{j,k} \\times p_{i,k}(1 - p_{i, k})\n",
    "$$\n",
    "\n",
    "as a means of estimating the effect of a predictor $j$ on individual $i$ ($x_{i, j})$ with respect to predicting the $k^{th}$ class and thus *locally* interpret the model. The average marginal effect is more robust measure of effects in situations where effects are compared across different groups or models. Consider a logistic model where an independent variable $x_1$ is used to predict an outcome and a logistic model where $x_2$, known to be uncorrelated with $x_1$, is also included. Since the two models assign different probabilities to the different outcomes and since the distribution of the outcome across values of $x_1$ should be the same across the two models (due to the independence assumption), we expected the second model will scale the coeffcient of $\\beta_1$. Hence, the log-odds and odds ratios are not robust to unobserved heterogeneity so directly comparing the two across models or groups can be misleading. As discussed in [[2]](#References), the marginal effect is generally robust to the effect.\n",
    "\n",
    "\n",
    "The average marginal effect (AME) of a predictor\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\sum_{i=1}^{n}\\beta_{j,k} \\times p_{i,k} (1 - p_{i,k})\n",
    "$$\n",
    "\n",
    "is equivalent to simply using $\\beta_{j,k}$ to *globally* explain the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def issorted(arr, reverse=False):\n",
    "    \"\"\"\n",
    "    Checks if a numpy array is sorted.\n",
    "    \"\"\"\n",
    "    \n",
    "    if reverse:\n",
    "        return np.all(arr[::-1][:-1] <=arr[::-1][1:])\n",
    "    \n",
    "    return np.all(arr[:-1] <= arr[1:])\n",
    "\n",
    "def get_importance(class_idx, beta, feature_names, intercepts=None):\n",
    "    \"\"\"\n",
    "    Retrive and sort abs magnitude of coefficients from model.\n",
    "    \"\"\"\n",
    "    \n",
    "    # sort the absolute value of model coef from largest to smallest\n",
    "    srt_beta_k = np.argsort(np.abs(beta[class_idx, :]))[::-1]\n",
    "    feat_names = [feature_names[idx] for idx in srt_beta_k]\n",
    "    feat_imp = beta[class_idx, srt_beta_k]\n",
    "    \n",
    "    # include bias among feat importances \n",
    "    if intercepts is not None: \n",
    "        intercept = intercepts[class_idx]\n",
    "        bias_idx = len(feat_imp) - (np.searchsorted(np.abs(feat_imp)[::-1], np.abs(intercept)))\n",
    "#         bias_idx = np.searchsorted(np.abs(feat_imp)[::-1], np.abs(intercept)) + 1\n",
    "        feat_imp = np.insert(feat_imp, bias_idx, intercept.item(), )\n",
    "        intercept_idx = np.where(feat_imp == intercept)[0][0]\n",
    "        feat_names.insert(intercept_idx, 'bias')\n",
    "\n",
    "    return feat_imp, feat_names\n",
    "\n",
    "def plot_importance(feat_imp, feat_names, **kwargs):\n",
    "    \"\"\"\n",
    "    Create a horizontal barchart of feature effects, sorted by their magnitude.\n",
    "    \"\"\"\n",
    "    \n",
    "    left_x, right_x = kwargs.get(\"left_x\"), kwargs.get(\"right_x\")\n",
    "    eps_factor = kwargs.get(\"eps_factor\", 4.5)\n",
    "    xlabel = kwargs.get(\"xlabel\", None)\n",
    "    ylabel = kwargs.get(\"ylabel\", None)\n",
    "    labels_fontsize = kwargs.get(\"labels_fontsize\", 15)\n",
    "    tick_labels_fontsize = kwargs.get(\"tick_labels_fontsize\", 15)\n",
    "    \n",
    "    # plot\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    y_pos = np.arange(len(feat_imp))\n",
    "    ax.barh(y_pos, feat_imp)\n",
    "    \n",
    "    # set lables\n",
    "    ax.set_yticks(y_pos)\n",
    "    ax.set_yticklabels(feat_names, fontsize=tick_labels_fontsize)\n",
    "    ax.invert_yaxis()                  # labels read top-to-bottom\n",
    "    ax.set_xlabel(xlabel, fontsize=labels_fontsize)\n",
    "    ax.set_ylabel(ylabel, fontsize=labels_fontsize)\n",
    "    ax.set_xlim(left=left_x, right=right_x)\n",
    "    \n",
    "    # add text\n",
    "    for i, v in enumerate(feat_imp):\n",
    "        eps = 0.03\n",
    "        if v < 0:\n",
    "            eps = -eps_factor*eps\n",
    "        ax.text(v + eps, i + .25, str(round(v, 3)))\n",
    "    \n",
    "    return ax, fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now retrieve the estimated coefficients, and plot them sorted by their maginitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = classifier.coef_\n",
    "intercepts = classifier.intercept_\n",
    "all_coefs = np.concatenate((beta, intercepts[:, None]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_idx = 0\n",
    "feat_imp, feat_names = get_importance(class_idx, \n",
    "                                      beta, \n",
    "                                      feature_names,\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_0_fig = plot_importance(feat_imp, \n",
    "                                 feat_names, \n",
    "                                 left_x=-1.,\n",
    "                                 right_x=1.25,\n",
    "                                 xlabel = f\"Feature effects (class {class_idx})\",\n",
    "                                 ylabel = \"Features\"\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that these effects are with respect to the model bias (displayed below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24013981,  0.66712652, -0.90726633])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that features such as `proline`, `flavanoids`, `od280/od315_of_diluted_wines`, `alcohol` increase the odds of *any* sample being classified as `class_0` whereas the `alcalinity_of_ash` decreases them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp, feat_names = get_importance(1,            # class_idx \n",
    "                                      beta, \n",
    "                                      feature_names,\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows that, however, `alcalinity_of_ash` increases the odds of a wine being in `class_1`. Predictors such as `proline`, `alcohol` or `ash`, which increase the odds of predicting a wine as a member of `class_0`, decrease the odds of predicting it as a member of `class_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_1_fig = plot_importance(feat_imp, \n",
    "                                 feat_names, \n",
    "                                 left_x=-1.5,\n",
    "                                 right_x=1,\n",
    "                                 eps_factor = 5,  # controls text distance from end of bar for negative examples\n",
    "                                 xlabel = \"Feature effects (class {})\".format(1),\n",
    "                                 ylabel = \"Features\"\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp, feat_names = get_importance(2, # class_idx\n",
    "                                      beta, \n",
    "                                      feature_names,\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, for `class_2`, the `color_intensity`, `ash` are the features that increase the `class_2` odds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_2_fig = plot_importance(feat_imp, \n",
    "                                 feat_names, \n",
    "                                 left_x=-1.25,\n",
    "                                 right_x=1,\n",
    "                                 xlabel = \"Feature effects (class {})\".format(2),\n",
    "                                 ylabel = \"Features\"\n",
    "#                                   eps_factor = 5.\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply KernelSHAP to explain the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='src_1'></a>\n",
    "Note that the *local accuracy* property of SHAP (eq. (5) in [[1]](#References))  requires\n",
    "\n",
    "$$\n",
    "f(x) = g(x') = \\phi_0 + \\sum_{j=1}^D \\phi_j x_j'.\n",
    "$$\n",
    "\n",
    "Hence, sum of the feature importances, $\\phi_j$, should be equal to the model output, $f(x)$. By passing `link='logit'` to the explainer, we ensure that $\\phi_0$, the *base value* (see _**Local explanation**_ section [here](kernel_shap_wine_intro.ipynb)) will be calculated in the correct units. Note that here $x' \\in \\mathbb{R}^D$ represents a *simplified input* for which the shap value is computed. A simple example of a simplified input in the image domain, justified by the dimensionality of the input space, is a *superpixel mask*: we formulate the task of explaining the outcome of an image prediction task as determining the effects of each superpixel in a segmenented image upon the outcome. The interested reader is referred to [[1]](#References) for more details about simplified inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 142 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={\n",
       "  'name': 'KernelShap',\n",
       "  'type': ['blackbox'],\n",
       "  'task': 'classification',\n",
       "  'explanations': ['local', 'global'],\n",
       "  'params': {\n",
       "              'link': 'logit',\n",
       "              'group_names': None,\n",
       "              'grouped': False,\n",
       "              'groups': None,\n",
       "              'weights': None,\n",
       "              'summarise_background': False,\n",
       "              'summarise_result': None,\n",
       "              'transpose': False,\n",
       "              'kwargs': {}}\n",
       "            ,\n",
       "  'version': '0.7.1dev'}\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_fcn = classifier.predict_proba\n",
    "lr_explainer = KernelShap(pred_fcn, link='logit')\n",
    "lr_explainer.fit(X_train_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.33066907e-16  0.00000000e+00  3.33066907e-16]\n"
     ]
    }
   ],
   "source": [
    "# passing the logit link function to the explainer ensures the units are consistent ...\n",
    "mean_scores_train = logit(pred_fcn(X_train_norm).mean(axis=0))\n",
    "print(mean_scores_train - lr_explainer.expected_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80a29e0539ed4a3f902bde0b4860e32f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_explanation = lr_explainer.explain(X_test_norm, l1_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the dimensionality of the feature space is relatively small, we opted not to regularise the regression that computes the Shapley values. For more information about the regularisation options available for higher dimensional data see the introductory example [here](kernel_shap_wine_intro.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Locally explaining multi-output models with KernelShap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaining the logitstic regression model globally with KernelSHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise the impact of the features on the decision scores associated with class `class_idx`, we can use a summary plot. In this plot, the features are sorted by the sum of their SHAP values magnitudes across all instances in `X_test_norm`. Therefore, the features with the highest impact on the decision score for class `class_idx` are displayed at the top of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(lr_explanation.shap_values[class_idx], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the logistic regression model uses a linear predictor function, the exact shap values for each class $k$ can be computed exactly according to ([[1]](#References))\n",
    "$$\n",
    "\\phi_{i,j}(f, x_i) = \\beta_{j,k}(x_{i,j} - \\mathbb{E}_{\\mathcal{D}}[x_{j}]).\n",
    "$$\n",
    "\n",
    "Here we introduced an additional index $i$ to emphasize that we compute a shap value for *each predictor* and *each instance* in a set to be explained.This allows us to check the accuracy of the SHAP estimate. Note that we have already applied the normalisation so the expectation is not subtracted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_shap = beta[:, None, :]*X_test_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z9QP6AXTs2LHnkCG5dky9opqaGtq0aVP/gBtZnOKNU6wQr3jjFCvEK96yj7W2lnWffZaN7ryTpa1a8fJdd9G6fft6rapPnz5j3L1XgSMsPneP1UDoWXgK8BWhB9d5hI4R0y7Ts2dPr68RI0bUe9lSiFO8cYrVPV7xxilW93jFW9axfvKJ+29+4w7uu+/u/vnnDYoXGO1lcN7Pd4jdPTZ3v8DdO7t7F+D3wHBXL9oi0tS5w9FHw7hxcO+98NJLsNFGpY6qJOJ6j01ERAA++AA22ABWXx3uuw/atYN11y11VCUVuxJbIncf6Snur4mIVLyFC+HSS6FnT7jyyjBuyy2bfFIDldhEROJn1Cg44QSYMCFcfrzwwlJHVFZiXWITEWly7rkHfvUrmDsXnn8eHnwQ1lij1FGVFSU2EZE4WLw4/N1jDzj9dPjwQ9h339LGVKaU2EREytns2eGy40EHhZqPXbrALbfAaquVOrKypcQmIlKunnoqVAh54AHo3h2WLi11RLGgyiMiIuXmhx/glFPgscdCQnvuOdh221JHFRsqsYmIlJtmzWDMGLjmGnjnHSW1PKnEJiJSDr75Bm64IQzt2oWq/KuuWuqoYkklNhGRUqqthdtvh65dQ8sh48aF8Upq9abEJiJSKh9/DL17w6mnwk47hSr8229f6qhiT5ciRURKwR2OOQY++QT++U849lgwK3VUFUGJTUSkMY0bF55Fa9cO7r8f2reHtdcubUwVRpciRUQaw4IFcNFF0KvX8kaLt9hCSa0IVGITESm2N98MrYd8/DEcdxxcfHGpI6poKrGJiBTT3XfDrruGEtuwYeF+WocOpY6qoimxiYgUw6JF4e9ee8GZZ4Yaj3vtVdqYmgglNhGRQpo1C44/fnmjxRtsADfeCG3alDqyJkOJTUSkUJ54IjRa/NBDoWdrNVpcEqo8IiLSUN9/DyefHBJbjx7w3/+GxoulJGJZYjOzlmb2jpm9b2YfmdkVpY5JRJqwqqrwfNrAgfD220pqJRbXEttCYDd3rzGzKuANM3vB3UeVOjARaSK++goGDYKbboLVVw+NFrdoUeqohJiW2DyoiV5WRYOXMCQRaSpqa+Hvf4ettoIHH4T33w/jldTKRiwTG4CZNTOzccB3wEvu/naJQxKRSjdxIj3OOAP+8pfwbNpHH8F225U6Kkli7vEu6JhZO+Ap4HR3/zBhfD+gH0DHjh17DhkypF7rr6mpoU2MqunGKd44xQrxijdOsUJM4nVn21NPpeWUKXx+2mnM2HPPWDRa3JB926dPnzHu3qvAIRWfu8d+AC4Fzkk3vWfPnl5fI0aMqPeypRCneOMUq3u84o1TrO5lHu+YMe6zZ4f/J03yN554oqTh5Ksh+xYY7WVwjs93iOWlSDNbKyqpYWbVwJ7ApJIGJSKVZf58GDAg9I9W12jxZpuxWM1hlb241opcB3jAzJoR7hM+6u7PljgmEakUr78Of/pT6CvthBPgkktKHZHkIZaJzd0/AHqUOg4RqUB33hketu7SBV56CfbYo9QRSZ5ieSlSRKTgFi4Mf/fdF845JzRarKQWS0psItK0/fADHHMMHHhgaLR4/fXDg9etW5c6MqknJTYRaZrc4dFHQy/W//oX7LijGi2uELG8xyYi0iAzZ0K/fjB0aGiF/+WXYeutSx2VFIhKbCLS9LRoEVoNGTQIRo1SUqswSmwi0jR88QWcckro2Xr11UNiO+ccaK4LV5VGiU1EKtvSpXDzzdCtGzz8MHzwQRhfVVXSsKR4lNhEpHJNmAC77AJnngl9+oRSWq/4NX0o+VEZXEQqkzscfzx8/nkoqf3hD7FotFgaTolNRCrL6NGw8cbQvn3oL619e/jFL0odlTQiXYoUkcowbx6cey7ssANcdVUYt9lmSmpNUL0Tm5kdVshARETqbeRI2GYbuOGG0GjxpZeWOiIpoYaU2E6u+8fMvjSzJ83sMjPra2YbFiA2EZHs7rgjVAyprYVXXoG77oJ27UodlZRQve+xufvuCS9PALaJhr7AFma2APgAeA8YDjwTdVwnItJwCxZAy5aw//5w/vmhlNaqVamjkjJQkMoj7j6ckLwAMLPmwBaERNcDuAHYCzitENsTkSZs5kzo3z/8HTYM1lsPrruu1FFJGSlK5RF3X+Lu4919sLufDfQklOREpMIMHTuVna8bzoYDnmPn64YzdOzU4mzIHYYMgS23hMceg513VqPFklLeic3MzjCzCWY218y+MbOnzOyIqDfrlNx9LnBqgyIVkbIzdOxULnhyPFPnzMeBqXPmc8GT4wuf3L77Dvr2Dc+ibbQRvPceXHaZmsOSlPJKbGZ2IXAzsDEwCZgL7As8BLxnZhunW9bd/1P/MEWkHA0a9jHzF69Yapq/eCmDhn1c2A21bAmTJsGNN8Jbb8FWWxV2/VJRMiY2MzvYzDZKGHUK8C6wtrtv5+5dgV8QakiuC4wws7WKFq2IlJVpc+bnNT4vn38OJ50UGi1ebbXQo/VZZ0GztBeHRIDsJbangE+jy46jgHWACcD6UQUR3P0nd78b2BaoAi4vYrwiUkbWbVed1/icLF0aSmbduoV7auPHh/FqtFhylC2xHQBcAAwFWkbzH0Oowj/XzN41szvM7E+EkttdNEIlETNbz8xGRPf6PjKzvxR7myKysnP33ozqqhVLUNVVzTh3783qt8IPP4SddgrdyeyxR2jEuGfPAkQqTUnGO6/u/jzwfN1rM5sF/JvwfFpPQinteKAfUPeMmpvZ3cBHwIfAh+4+vcBxLwHOdvf3zKwtMMbMXnL3CQXejohk0LdHJyDca5s2Zz7rtqvm3L03WzY+L+7wpz/BV1+Fktrhh6vRYqmXfKsUDQd+BfR39/8HYGYtgG5AL+BQYHfgaKBFtIwDBb0o7u7fAt9G/881s4lAJ8JlUhFpRH17dKpfIqvzzjs0nzs3JLGHHgqNFq+5ZuEClCYn3+r+lwGbAK+aWTcAd1/k7mMIlyFrgFlAa6Ar8Hvg6sKFuzIz60J4CPztYm5HRAps3jw4+2zYaSc2ePDBMG7TTZXUpMEs31auzGxfYAjQhlDlfywhoe1CaG3kbnc/Of0aCsfM2gCvAte4+5NJ0/oRLpHSsWPHnkOGDKnXNmpqamjTpk1DQ200cYo3TrFCvOIt91jbjR3LZjfcQPW0aUw96CDGH3kkLWPSCn+579tkDYm3T58+Y9w9fj2zunveA6Fq/03A10BtNCwBBgPV9VlnPWKoAoYBZ2Wbt2fPnl5fI0aMqPeypRCneOMUq3u84i3rWG+/3R3cN9nEfeRIdy/zeJPEKVb3hsULjPZGOJ8XeqjXY/vuPg04Ezgzem5tDWCau//UsDSbGzMz4F5gorv/rTG2KSINVNdo8QEHwOTJcPHFarRYiqLBbUW6+0x3n9RYSS2yM6GCym5mNi4a9mvE7YtIrr77LjSFdeCBoebjeuvBtdcqqUnRxLKhNXd/A1A9YJFy5g6PPAJ/+Qv89BNcckl4+FrtO0qR6QgTkcKbMQP++Ed4/nnYcUe4997QKr9IIyhKtzUi0sRVV8MXX8DNN8MbbyipSaNSYhORwvj009ByyMKFodHi8ePDZUg1WiyNTIlNRBpmyRK4/nrYemt4/PHQ3iPoXpqUjI48kQo3dOzUwrTlmMr778MJJ8CYMaEj0Ntug3XXLcy6ReopY2Izs+H1WKe7++71jEdECqiuh+u6zkDrergGGp7c3KFfv/BM2qOPwqGHqtFiKQvZSmwbsbzV/jqtgbrG3OZEf9tFf78nNK8lImUgUw/X9U5so0bBL38JHTrA4MHh7xprFCBakcLIeI/N3bu4+4Z1A6Hl/vnALcC67t7B3TsQmtj6OzAvmkdEysDUQvZwXVMD/fvDr34F11wTxm26qZKalJ1877HdBLzl7mcmjvTQ31p/M1s7mqdvYcITkfoaOnYqxsqXXKAePVy/9FK47PjVV3DqqXD55Q0PUKRI8q0V2ZvQmn46I6N5RKTEBg37OGVSM8ivh+vbboO99oIWLeC11+DWW6Ft20KFKVJw+ZbYnNA1TTpdGxCLiBRQusuNTo4VR+bPDw9aH3wwTJ8OF14YXouUuXxLbC8Cp5jZMVEL+0Bobd/MjgVOiuYRkRJbvboq5fh2acYvM2MGHH748kaLO3eGq65SUpPYyLfEdhawHfBP4Doz+zQavynQEZgczSMiBZTLs2iJ8wzoXsvipakTWNoa+e7w0EOhgsi8eXDZZVBbq5ZDJHbySmzuPsXMugPnAwcD20eTvgDuB6539zkFjE+kycvlWbTkeRYtreXnRUtTrm/2vMUrj5wxA447Dv7731Dr8d57YfPNC/9mRBpB3k1qufuP7n6hu3d19+po6BqNm1OEGEWatEzPomWaJx0jJMIVVFfD11/D3/8Or7+upCaxVu+2Is1sVTPrZGYtChmQiKwoXSWQxPH5PJfmhETIxx+HrmUSGy0+/XRYRU3ISrzlfQSb2bZRU1tzgW+AXaLxvzCzV8xsjwLHKNKkpXvmLHF8Ps+lNV+6hIP++yBssw089RR89FGYoHtpUiHySmzR/bXXgY2BBxOnuft3QDVwbKGCE5HwzFl11YpJp7qq2QrPoqWbp32rFSuQdJ3xOUMfOpvzX30A9t8fJk6EbbctXvAiJZBvie1KYBrhebUBhMv1iV5heYUSESmAvj06MfCQbnRqV40BndpVM/CQbivUikyep0WzVRh4SDcuO7Dr8oTnztXDbqdjzSzeGXQnPPEErL12Sd6TSDHlW91/V2Cgu9eY2aoppn9DaDeyqMzsPuAA4Dt336rY2xNpbKmq9785YLeMy/Tt0WlZshs5ciS9o/87vD+agZ/XMmlxCwYeeTHH7t+d/XvrayOVK9/E1hL4McP01RoQSz7uB24l6XKoSCVIVb3/zH+PY/TXs7i6b7fcV1RTAxdeyK9vvZVfn3km3HhjkSIWKS/5Xor8HOiZYfpuwIT6h5Mbd38NmFXs7YiUQqqq+w48POqblavpp9H+nXega9fQruPpp8MVVxQhUpHylG9iewQ4OqnmowOY2dnAPsBDBYpNpEnK1MZj4rNrad16K9ucfz60ahWeSbvlFmjTprBBipQxc0/V/neamcMza8OAXwOTgM2B8cBawNrAS8B+7l5b+FBXiqUL8Gy6e2xm1g/oB9CxY8eeQ4YMqdd2ampqaBOjk0Kc4o1TrNB48X48fS6Llqb/CnXrtHrK8assWEBty5a0mDmTNZ96iunHHUdti3g8ZhqnYyFOsULD4u3Tp88Yd+9V4JCKLt8mtRaZ2Z7A6cCRwALgl8CnwN+AWxojqeXC3e8C7gLo1auX9+7du17rGTlyJPVdthTiFG+cYoXGi3fO2Kmc+e9xKbuc6dSumtOPTIrh22/htNNg9mx45RUwY+Raa2nfFkmcYoX4xVsI+VYewd2XEDoTvanw4Yg0HekaNu7boxOjv57Fw6O+WSG5JT+7hjs88ACceWboYuaKK9RosQh5Jraomv2d7v52munbAye7+x8LEVyGOP5F6NB0TTObAlzm7vcWc5si9ZUqgQEZGza+um83em3QIX2L/tOnwzHHhJ6td9kF7rkHNsuj81CRCpZvie044GUgZWIDNiS0PFLUxObufyjm+kUKJV3L/Ks2XyVtw8Z1ySvxubSVtG4N06aF3q1PPlntO4okKPS3oTWQok8MkaYpXcv8c+an/ppkbMx44kQ49tjQaHHbtvD++/DnPyupiSTJWmIzs/WBLgmjNjezX6eYtQNwCvBZYUITib98Wt2HNI0ZL14M118PV14Zqu1PmAA9euhemkgauVyKPB64jPAYjQMXRUMyA2qj+UWEkKimpkhu7VtVsWBx7QqluZUqhwCMGRO6lvngAzj88NBfWseOxQ5bJNZySWxDga8Iies+QhX6/yXN40AN8K67Ty5gfCKxdu7em61wjw1CArvswK4A6SuHQKj1eOqp8N13oXuZvn0bOXqReMqa2Nz9feB9ADPbAHjC3T8sdmAilaAuUaVLYCkrh7zxBmyxBayxBjzyCLRvHwYRyUm+D2irwTmRPGWs3Zho7lwYMABuvx3OOis0WrzRRsUPUKTC5P2ANoCZ9QJ2ANqzcs1Kd/erGhqYSJPywgtw0kkwZQr07x8qiohIveT7gHY18CSwF+Gem7O8s1FPGKfEJpKrf/wDzjgDttwS3noLdtyx1BGJxFq+JbZLCUntGkJv2SMID2R/B1wAVAPHFDJAkYrkDj//HKrvH3JIaOfx/PNh1VT994pIPvJ9svNQ4DF3vxSoq0Ay1d2HAXsALQitk4hIOtOmwW9/CwceGBJcp05w6aVKaiIFkm9iWw94Nfq/rv5yC1jWOPK/gN8XJjSReBk6dio7XzecDQc8x87XDV+5U1B3uPfecMlx2DDYf//QaLGIFFS+lyLnJiwzl/BA9roJ038k9MsmUnHmzF/MztcNT1ltP12bkBBV6f/2Wzj66NCtzG9+Exot3mSTvGNI1yOAiCyXb2L7nND/Gu6+1Mw+IlyevM/MDDgE0APaUnGGjp3K1NnzmTonNGOVnLjStQm5rFHjtm3Dg9Z33AEnnphz+46JiaxdqypqFixhca2njEFEgnwvRb4M/M7M6hqpuxPYx8w+J3Q2ugeg7mOk4gwa9jG1Sb3N1yUuSN0m5KYzv+acwVeHRovbtIFx40KV/jyS2gVPjmfqnPk4MHve4mVJLVUMIhLkW2K7DniIqIq/u99uZi2Bowj33O4Gri9ohCJlYOqc+eEOc6rxrNgmZNXSxZw86nFOf+vfzGvZKrTK3717yoSW6dJiqlJgKvk2tCxS6fIqsbl7jbt/HFUUqRv3N3ff1t23c/e/unuqHu1FYq2ZWcbx5+69GdVVzdj62094+oEzOfuNh3lp851565nXQ1JLIblEVndpsa7SSa4JK2WPACJNWL1aHhFpapam+b1WN75vj07gziZ9z6L9/J84/+ir2OnM4zPe+8p2Xy5dzwCJUvYIINLEqYdCkRx0SlMq6tSuGl59FX74gb7bdmarV59j7Slf8NcHLwbIWP0/XYmsbnxdKTBRVTOjXXUVFm174CHdVHFEJEneJTYzOwI4FdgUWCPFLO7uKglKRTl3782YOnHMCuPWWrqAwaOHwAWDlzdavOGGQA7V/0nfV1vdpcVsPQOISGr5thV5MXAFMAN4C5hdjKByjGUf4BagGXCPu19Xqlik8vXt0Ymh0yfQqV0zps2Zz6HfjuPK/95K9fffwdlnr9Rocdbq/6Tvqy3x0mLOPQOIyDL5lqz+DIwE9nH3xYUPJzfR4wa3AXsCU4B3zexpd59Qqpik8rWrruLNAb3hllvgrxfDVlvBM/+B7bdfad5slxlBJTKRYsk3sa0GPFrKpBbZHvjM3b8AMLMhwMGAEpsUhzvN5s0L/x92GNTUwLnnQosWKWfPdpmxjkpkIoWXb+WRsaR8mqfRdWLFFk6mRONECm/qVDj4YLpdeGFo23HddeGii9ImNUhd8UM1GEUah+Xz2JmZ/QZ4AtjT3ccWLarscRxKuBz6p+j10cAO7n5awjz9gH4AHTt27DlkyJB6baumpoY2bdo0POhGEqd4yz7W2lrWee45Nr7zTmzJEiYddRQz//AHaNYs+7KEtiVn/LiARUtradFsFTqu3pJ21VVFDjoo+32bJE7xxilWaFi8ffr0GePuvQocUtHldSnS3V81sxOAUWY2CviK5a38J8zmJxQovnSmsmLJsXM0LjGIu4C7AHr16uW9e/eu14ZGjhxJfZcthTjFW9axTpsGRx4JI0dCnz5w993MnDy5fONNUtb7NoU4xRunWCF+8RZCvrUidwAeAKqAXaMhmQPFTmzvApua2YaEhPZ74Igib1OaktVWg1mz4O674YQTwAwmq31vkTjIt/LILcAiQkWN1919TsEjyoG7LzGz04BhhOr+97n7R6WIRSrI+PEwcCDcd19otHjs2LQNFqv7GJHylW9i2xq43N2fKUYw+XD354HnSx2HVICFC+Haa8PQvj18/DFss81KSa2uP7apc+ZjhEsToO5jRMpNvrUivyOU2EQqw9tvQ8+e4QHr3/8eJkwISS3J8v7YQhX+5CpX6j5GpHzkm9juA44yMzWZJfHnDv37M+/7WZxz7DVs2On37HzPByu16Qip+2NLpu5jRMpDvgnqDeAAQq3I24EvWblWJO7+WgFiEymOESNg661hjTV48eKbuPDVqXy/Sksg/WXFaWn6Y0uk7mNEykN9etDuBWwL3BO9HpEwjIz+ipSfOXPgxBNht91CJRHgivHzliW1OqkuK2ZLWnr4WqR85FtiO74oUYgU29NPwymnwPTpcN55cPnlQG5tOkLq1v3rKpB0Uq1IkbKS7wPaDxQrEJGiuflmOPNM6NYN/vMf6LW8IYV82nRMbN1fVfxFypcqgUhlcoe5c8OD1ocfDvPnh+5lktp3zKXrmDrLWvcXkbKWMbGZ2THRvw+5uye8zsjdH2xwZCL19c03cPLJMG8eDB8eGi2+4IKUs6rrGJHKk63Edj/hNsIQwvNrda8twzIOKLFJ46uthTvvDPfQamuXVRDJRl3HiFSWbImtD4C7L0p8LVIu6pq2Wjp5Mv/vhb/R48sPYI894K67YMMNSx2eiJRAxsTm7q9mei1SSkPHTl12f6zVqq2pmvczFx5wJttfcRZ9N+xc6vBEpETyeo7NzO6LWvhPN317M7uv4WGJZPfU/c9z/ePXsuqSRcxrUc2Bx97EI113Z9CLn5Q6NBEpoXwf0D4O2DjD9A2BY+sdjUguFi6ESy7hnltPYcdvxrPRrCkAuIXDWU1biTRtha7u3xpYXOB1iiz3v/+F/tEmTuTlHntywa7HM6d6tRVmSddKiLqaEWkasiY2M1sf6JIwanMz+3WKWTsApwCfFSY0kSTucNZZ8PPP8MILLOzYjYVPjoccnkFLvB8H6mpGpJLlUmI7HriMUI3fgYuiIZkBtajZLSm0l1+G7t1hzTVhyBDo0AHatqVvNPnypz9izvxwoaBlVeqr64OGfbzCQ9iwvE1IJTaRypJLYhsKfEVIXPcBdwH/S5rHgRrgXXefXMD4pCmbPTu0FvLPf4a/N9wAG2yw0mwLl9QuX2Te4vSt86eg+3EilSdrYnP394H3AcxsA+BJdx9f7MCkiXvqKfjzn2HmzNBqyKWXppwt15JYrm1Cikj85dsI8hXJ46JORw8m3GN7xt2nFyg2aar+9rdQQuveHZ5/Hnr0SDtruhLX1Dnz2fm64csqivTZfC2eGDM1pzYhRSTe8n2O7XozezfhtRH6ZHsUuBMYb2aZHgdoMDM7zMw+MrNaM+uVfQmJBXf48cfw/x/+ANdfD++8kzGpQfoSlxGSm0d/nxgzld/17ESndtUYoauZgYd00/01kQqUb3X/fQiJrM6BwK+B64FxwD+AAcCJhQgujQ+BQwiJVCrB11/DSSeFFvhHjIB11oFzz81p0VSt89f1k5Zo/uKljJg0kzcH7Fa4uEWkLOWb2NYDPk14fSDwpbsPADCzrsCRBYotJXefGG2rmJuRxlBbC7fdBgMGhBLbddfltXjdc2nzFy+lmRlL3Zf9TUUVRUSahnwTWwtgScLrPqxYgvsCWKehQUkTMGUK3fv3h/HjYe+9Q6v8KWo8ppP8XFpdMkuX1ABWr65qUMgiEg/mGU4EK81sNgl4y93/GJXOxgNHufsj0fTzgXPcfa0GBWX2MrB2ikkXuft/onlGRtsanWYd/YB+AB07duw5ZMiQesVSU1NDmzZt6rVsKcQl3mbz59Pt9NP59tBDmbH33pBnCfzj6XNZtLQ2+4wJzIzO7atpV88EF5d9C/GKFeIVb5xihYbF26dPnzHuHru6DPmW2IYAl5jZL4CuwE/A8wnTewCfNzQod9+jAOu4i/DMHb169fLevXvXaz0jR46kvsuWQlnHO3Zs6CPtgQegupqRd99N7z592KIeqzp+wHN43k2dQqd2zerdC3ZZ79skcYoV4hVvnGKF+MVbCPmeGQYSOhvdiXB//hh3nwNgZqsDBwGvFDA+qQQLFsCFF8J228Hrr8On0W3aBtwnre/zZ7rPJlL58kps7r7Q3U9w9zXcfSN3fzph8lzC/bXLCxlgMjP7rZlNISTX58xsWDG3Jw305pvhebSBA+GYY2DCBNh66wav9ty9N6O6qlney+mBbJHKV7DW/d291swWE2pOflGo9abYzlPAU8VavxSQe6i2v3AhvPgi7Lln1kWytcCfOH316ioWLFlKqtvEZtDcjMW1yyfqgWyRpiFric3MFpnZ7xNetzWzp82sW4rZf8uKjwNIU/Tii6EpLLPQaPH48TkntQueHL/Cg9UXPDmeoWOnppw+Z/5imq9iVK2y4iXN6qpm3HR4dwYdto0eyBZpgnIpsTVnxQTYAjgAuLkYAUmMzZoFZ54JDz4I55wDgwbB+utHpax3svaDlq3dx1TTFy912reqolWL5inXr0Qm0vQUuqNRaaoefxxOPTUkt4sugosvBjL3g9YuaRXZWuBPN33OvMWMvXSvhr8HEakI+deXFkn2t7/BYYdB587w7rtw9dXQsiWQuRSWLF3Fjrrx2aaLiIASm9SXO8yZE/4/4ojQV9rbb4cakAny6QctVU3HxAof2aaLiIASm9THl1/CXnvBgQeG9h7XXjt0M9N85Svb+ZSy+vboxMBDuqWt8JFtuogI5H6PbT8zq2viqhXh4ezDzKx70nw9CxWYlKGlS0OjxRdcAKusErqWySJV6/vLSlk/rlyBtm+PThkTVbbpIiK5JrYjoiHRSWnmzb3xSYmPKVPg8MPhf/+DffeFO+6A9dcHMj97Vvc31fSRI9M/GZLteTYRkXRySWx9ih6FlL/27WHJEnjoITjyyGXNYWWq9ZiY3PJJSrmsU0QknayJzd1fbYxApAyNGROawnroIWjdOlQOSWrfMduzZ/WRbp39/z2OQcM+VulNRDJS5RFZ2fz5ofPPHXaAt96Czz4L41M0WpxPrcdcZVo2uTUSEZFkSmyyotdeg222gb/+FY47LjRa3C1V62lBMZ4ty7ZsuufgRERAiU0SuYeS2pIl8PLLcM890K5dxkWK8WxZLi33q/sZEUlHTWoJvPAC9OwJv/gF/Pvf0KFDuKeWg0y1HusrcZ1T0yQwtTYiIukosTVl338fGi0ePHh5o8XrrZf3aorxbFndOpNrSIJaGxGRzJTYmiJ3ePRROP10mD0bLrssPHRdhopRIhSRyqbE1hTdeGPoALRXL3jllYyVQxpLtoe8lchEJFdKbE1FXaPF7dvDUUeFdh1POy1l+46NTQ9ki0ghqVZkU/DFF7DHHnDQQcsbLe7fvyySGuTXtY2ISDZKbJVs6VK46SbYaqvQT9rRR5c6opSK8ZC3iDRdsUtsZjbIzCaZ2Qdm9pSZtSt1TGVp8mTYeWc46yzYbbfwoHW/fqFV/jKjDkRFpJDK7yyX3UvAVu6+NfAJUJ7V+UptjTXC30cegWeeCb1blyl1ICoihVQeN1ny4O4vJrwcBRxaqljKzrvv0vWSS+DFF6FVq9DFTIr2HcuNqvSLSCGZe3y7TzOzZ4B/u/vgFNP6Af0AOnbs2HPIkCH12kZNTQ1t2rRpUJzFtsqCBWz4z3/S+fHHWdC+PR8OGsTPG25Y6rCyisO+TRSneOMUK8Qr3jjFCg2Lt0+fPmPcvVeBQyo+dy+7AXgZ+DDFcHDCPBcBTxEl50xDz549vb5GjBhR72UbxYgR7htv7A7uJ53krz3zTKkjylnZ79skcYo3TrG6xyveOMXq3rB4gdFeBjkh36EsL0W6+x6ZppvZccABwO7Rzm+a3OGii8L/w4dDnz4sHTmypCGJiJRaWSa2TMxsH+A84DfuPq/U8ZTEc8/Bdtut2Ghxq1aljkpEpCzEsVbkrUBb4CUzG2dmd5Q6oEYzcyYccQQccADccEMY17mzkpqISILYldjcfZNSx9Do3GHIEDjjDPjxR7jiitBvmoiIrCSOJbam54YbQklt441h7Fi49FJo0aLUUYmIlKXYldiajNra0Ghxhw5wzDGw6qpw6qnQLHPP0iIiTZ1KbOXos89g993hwANDe48dO4bLkEpqIiJZKbGVkyVLwmXHbt3CJcc//rEs23YUESlnuhRZLr75Bn73Oxg9Gg4+GG6/HdZdt9RRiYjEjhJbuVhzTaiqCs+lHXZYLNp4FBEpR7rOVUpvvx1KZ/PmhWfR3nwTDj9cSU1EpAGU2Erh559DP2k77QTvvQdffhnGK6GJiDSYEltje+WVUDnkppvg5JPho4+ga9dSRyUiUjF0j60xuYeHq5s3h1dfhV//utQRiYhUHCW2xvD007DDDuF5tEcfDQ9dV1eXOioRkYqkS5HFNGMG/N//hQoiN94YxnXqpKQmIlJEKrEVgzsMHgz9+0NNDVx9NZx3XqmjEhFpElRiK4brrw/tO262GYwbFzoDraoqdVQiIk2CSmyFUlsLs2fDGmvAccdBmzah1qPadxQRaVQqsRXCJ59A794rNlqslvhFREpCia0hliwJlx232QbGj4cTT1SjxSIiJaZLkfX19ddwyCGh5ZDf/hZuuw3WWafUUYmINHlKbPW11lqh2v5jj4VW+dUclohIWYjddTMzu8rMPjCzcWb2opk1Xt8ub70FBxywvNHi11+HQw9VUhMRKSOxS2zAIHff2t27A88ClxZ9izU1oQfrXXYJ99LUaLGISNmKXWJz958SXrYGvJjba//uu7DVVvCPf4Sajh9+qEaLRUTKWCzvsZnZNcAxwI9An6JtyJ0uDz4ILVuGy4677FK0TYmISGGYe1ELPPViZi8Da6eYdJG7/ydhvguAlu5+WYp19AP6AXTs2LHnkCFD6hXL4q+/ZtV11qG2RYt6Ld/YampqaNOmTanDyEmcYoV4xRunWCFe8cYpVmhYvH369Bnj7r0KHFLxuXtsB2B94MNs8/Xs2dPra8SIEfVethTiFG+cYnWPV7xxitU9XvHGKVb3hsULjPYyONfnO8TuHpuZbZrw8mBgUqliERGR8hPHe2zXmdlmQC3wNXByieMREZEyErvE5u6/K3UMIiJSvmJ3KVJERCQTJTYREakoSmwiIlJRlNhERKSiKLGJiEhFKcuWRwrNzGYSHg2ojzWB7wsYTrHFKd44xQrxijdOsUK84o1TrNCweDdw97UKGUxjaBKJrSHMbLTHqEmZOMUbp1ghXvHGKVaIV7xxihXiF28h6FKkiIhUFCU2ERGpKEps2d1V6gDyFKd44xQrxCveOMUK8Yo3TrFC/OJtMN1jExGRiqISm4iIVBQlthyY2VVm9oGZjTOzF81s3VLHlImZDTKzSVHMT5lZu1LHlI6ZHWZmH5lZrZmVZc0tM9vHzD42s8/MbECp48nEzO4zs+/M7MNSx5KNma1nZiPMbEJ0DPyl1DFlYmYtzewdM3s/iveKUseUjZk1M7OxZvZsqWNpTEpsuRnk7lu7e3fgWeDSEseTzUvAVu6+NfAJcEGJ48nkQ+AQ4LVSB5KKmTUDbgP2BbYE/mBmW5Y2qozuB/YpdRA5WgKc7e5bAjsCp5b5vl0I7Obu2wDdgX3MbMfShpTVX4CJpQ6isSmx5cDdf0p42Roo6xuT7v6iuy+JXo4COpcynkzcfaK7f1zqODLYHvjM3b9w90XAEEIHt2XJ3V8DZpU6jly4+7fu/l70/1zCCbhTaaNKL+pUuiZ6WRUNZXsuMLPOwP7APaWOpbEpseXIzK4xs8nAkZR/iS3RH4EXSh1EjHUCJie8nkIZn3zjysy6AD2At0scSkbRpb1xwHfAS+5ezvHeDJxH6JS5SVFii5jZy2b2YYrhYAB3v8jd1wMeBk4rbbTZ443muYhwuefh0kWaW6zSdJlZG+AJoH/S1ZGy4+5Lo1sSnYHtzWyrEoeUkpkdAHzn7mNKHUspxK4H7WJx9z1ynPVh4HngsiKGk1W2eM3sOOAAYHcv8TMdeezbcjQVWC/hdedonBSAmVURktrD7v5kqePJlbvPMbMRhPuZ5VhRZ2fgIDPbD2gJrGZmg939qBLH1ShUYsuBmW2a8PJgYFKpYsmFme1DuARxkLvPK3U8MfcusKmZbWhmLYDfA0+XOKaKYGYG3AtMdPe/lTqebMxsrboaxmZWDexJmZ4L3P0Cd+/s7l0Ix+zwppLUQIktV9dFl84+APYi1DQqZ7cCbYGXokcU7ih1QOmY2W/NbAqwE/CcmQ0rdUyJoko4pwHDCJUbHnX3j0obVXpm9i/gf8BmZjbFzE4odUwZ7AwcDewWHafjohJGuVoHGBGdB94l3GNrUtXo40Itj4iISEVRiU1ERCqKEpuIiFQUJTYREakoSmwiIlJRlNhERKSiKLGJiEhFUWITaQLMbD8zczP7baljESk2JTZpksysd3SiTzcsyb6W4jOz7mZ2edRIcEN0i/6Ob+B6RMqe2oqUpu5fhLY/k5VLi+jdCe2SjgS+asB6ugHzgC8aHJFImVNik6buPXcfXOogGkE34CN3L5eELVI0uhQpkoGZNTezN83sZzPbPGlav+iy5ZUJ49qa2dVm9raZfW9mC83sMzO7zsxapVh/CzM7L2oncZ6Z/Whmo83sNDO7HPhnNOuIhMuk9+cYe1cze9zMZgNbA72i9iMvqu/+EIkDldikqWtlZmumGL/I3X9y9yVmdgQwDhhiZju4+0Iz60royPEN4IqE5ToBfyJ0xfIIoT+83xB6W+gB7F03Y9RbwDCgN/AiMBhYQChdHQL0JzS82w+4ltAIM8Dn2d6Ume0bxfA58Gi0joeALYGrzexnd78523pEYsndNWhocgMhmXiG4dmk+Q+Jxt8KVBP64JoFrJ80XwugKsX2roqW3z5h3HnRuGtTzL9K9Pe4aJ7eeby3TsBPhBboq4E/R+vYFugALAImlfoz0KChWINKbNLU3QU8lmL8zMQX7v6kmf0/4FRCyasr8Dt3/yZpvkV1/5tZc0L3Qc2Al4GLgR2Ad6JZjgRmA1eSxBt2L+ycaLsnuvt8M9uWkMw+dPdFUbcrWyQuYGbDgTvd/d8N2K5IWVBik6buU3d/Ocd5zyL0x/cr4G5P0+Ozmf0ZOJmQ/JLvY7dP+H9TYJy7L8gv5Kx+RyiRjYte9yRKatHrVoSEuoy771bgGERKRpVHRHK3DbB+9P9WUYlsBWZ2FnAb8C1wErA/oafl46JZivqdM7NfAOsBY6PXqxIS7Jjo9erAxnWvRSqREptIDsxsNcIzb98DFxF6/L4ixaxHE54329fd73H356MS4YwU834CbB4ln3Ty7Ql4jehvTfS3G1DF8kR2OOE+4ON1C5jZ7mb2VZ7bESlbSmwiubkL2AA4yt2vJSSGAWbWJ2m+pYRkZHUjopLdgBTrfJhwafLi5AlmVrd8XYLqkGOc0wgPl+9iZqsQLkMCvGdm6wEDgY+AIQnLbEOo9SlSEXSPTZq6bc3sqDTThrp7jZmdAPwfofbi8GjaicB2wGAz29rdf4jGP05IHi+Y2ZPAasARwOIU678FOBC42My2I1T5X0C4dLgZsAehZmMtcJGZtQd+Br5097dTBezuP5rZkGibTwJtokkHEqr8LwQOcffEeLYB3k+zD0Tip9TVMjVoKMVA9ur+DmwCbE5IJm8CzZPWsRMhYT2dMK4ZcAHwGSGJfA1cT6iF6MDlSetoSbi0+REhqc0hJLM/J8xzLDCBULPRgfuzvLfWhBLmrGj+WsI9vzuAjinmH0dIdiX/XDRoKMRg7vlewheROIgugf4MPOzuf0wzTxXhcueW7p71wW+RONA9NpHK9UtCRZEPM8yzBaFkqcaRpWIosYlUrq2iv5kS2zbAB65LN1JBlNhEKlcuia0H8F4jxCLSaHSPTaSJMrO2hKR2rrsPLXE4IgWjEptIE2RmfYFJhDYsnyltNCKFpRKbiIhUFJXYRESkoiixiYhIRVFiExGRiqLEJiIiFUWJTUREKooSm4iIVBQlNhERqShKbCIiUlGU2EREpKL8f95c/QEBWtEUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_name = 'alcohol'\n",
    "feat_idx = feature_names.index(feat_name)\n",
    "x = np.linspace(-3, 4, 1000)\n",
    "plt.scatter(exact_shap[class_idx,...][:, feat_idx], lr_explanation.shap_values[class_idx][:, feat_idx])\n",
    "plt.plot(x, x, linestyle='dashed', color='red')\n",
    "plt.xlabel(r'Exact $\\phi_j$', fontsize=18)\n",
    "plt.ylabel(r'Estimated $\\phi_j$', fontsize=18)\n",
    "plt.title(fr\"Comparison of estimated and exact shap values for feature '{feat_name}'\")\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows that the exact shap values and the estimate values give rise to similar ranking of the features, and only the order of the `flavanoids` and `alcohol`features is swapped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(exact_shap[class_idx, ...], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An simlar plot can be create for the logistic regression model by plotting the marginal effects. Note that the plot labelling cannot be changed, so the x axis is incorrectly labeled as `SHAP value` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = classifier.predict_proba(X_test_norm)\n",
    "prb = p * (1. - p) \n",
    "marg_effects = all_coefs[:, None, :] * prb.T[..., None]\n",
    "assert (all_coefs[0, 0] * prb[:, 0] - marg_effects[0, :, 0]).sum() == 0.0\n",
    "avg_marg_effects = np.mean(marg_effects, axis=1) # nb: ranking of the feature coefs should be preserved\n",
    "mask = np.ones_like(X_test_norm)  # the effect (postive vs negative) on the output depend on the sign of the input\n",
    "mask[X_test_norm < 0] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(marg_effects[class_idx, :, :-1]*mask, X_test_norm, feature_names)  # exclude bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the ranking of the marginal effects is the same as that provided the ranking the raw coefficients (see below). However, this effect measure allows us to assess the effects at instance level. Note that both the approximate computation and the exact method yield the same group of features as the most important, although their rankings are not identical. It is important to note that the exact effects ranking and absolute values is a function of the entire data (due to the dependence of the model coefficients) whereas the approximate computation is *local*: the explanation model is fitted locally around each instance. We also notice that the approximate and exact shap value computation both identify the same relationship between the feature value and the effect on the evidence of a sample belonging to `class_idx`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_0_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 6 most important features for this classification in `class_0`, we see that both the `KernelSHAP` method and the logistic regression rank the `proline` feature as the one with the most significant effect. While the order of the subsequent 5 features is permuted, the effects of these features are also very similar so, in effect, similar conclusions would be drawn from analysing either output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#src_1) Lundberg, S.M. and Lee, S.I., 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).\n",
    "\n",
    "[[2]](#src_2) Mood, C., 2017. \"Logistic regression: Uncovering unobserved heterogeneity.\""
   ]
  }
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